Design of fuzzy control system for temperature and humidity in environmental laboratory

Traditional temperature and humidity closed-loop control usually uses switch control or PID control. The former is simple to implement but has poor accuracy, while the latter has high accuracy but requires the establishment of a mathematical model and high parameter setting requirements. In an environment with complex nonlinear changes in temperature and humidity, it is not easy to accurately model. Fuzzy control theory is an adaptive control technology that can simulate human brain intelligence and change with the environment. It is suitable for nonlinear systems and complex systems that are difficult to accurately describe with mathematical models. Further, a new control mode combining neural networks and fuzzy reasoning can be adopted.

1. Structure of environmental laboratory temperature and humidity monitoring system

The temperature and humidity monitoring and control mechanism of the environmental laboratory is shown in Figure 1. The signals measured by the temperature and humidity sensors are conditioned and input into the fuzzy control algorithm module to generate decision signals to control the drive components (heater, refrigerator, humidifier, dehumidifier) ​​to keep the temperature and humidity of the environmental laboratory constant at the set value.

2 Fuzzy control mechanism of control system

Typical fuzzy logic control consists of three parts: fuzzification, fuzzy reasoning and clarification. The following is a specific explanation using temperature control as an example. Based on the traditional fuzzy control model, the principle of the temperature fuzzy control system in this design is shown in Figure 2.

The fuzzy controller uses a dual-input single-output control method, with temperature error e and error change rate ec as input variables and u as output variable. The fuzzy subset is E=EC=U={NB, NM, NS, ZE, PS, PM, PB}={negative large, negative medium, negative small, zero, positive small, positive medium, positive large), and its domain is: e=ec=u=[-3, 3]={-3, -2, -1, 0, 1, 2, 3}. The membership function uses a triangular distribution function, as shown in Figure 3.

According to the input/output characteristics of the control system, the control rules are formulated with the elimination of temperature deviation as the control target as shown in Table 1.

Reasoning from fuzzy rules can derive the input-output relationship of the fuzzy controller language rules, and the relationship is a nonlinear relationship surface. When the deviation is large, the change of the control quantity should try to reduce the deviation quickly; when the deviation is small, in addition to eliminating the deviation, the stability of the system should also be considered to prevent the system from overshooting and even causing system oscillation. The control query table can be obtained by using the Mamdani reasoning method and the area centroid method to clarify the membership function and the rule table.

The actual meaning of the corresponding output quantity U is shown in Table 3.

Note: √ means start; × means not start
Working mechanism: According to the two-dimensional constant array established by the fuzzy control query table, the input deviation E and the deviation change rate EC are quantified to its basic variable domain, and the query table is retrieved in real time as the rows and columns of the array to obtain the real-time output U. According to the actual meaning of the output U, the heater or refrigerator is controlled, thereby driving the temperature to stabilize at the set value.

3 Control system programming

The program is designed in ST language, including the main program, fuzzy control algorithm, interrupt service program, operation command and alarm program. The flow chart of the fuzzy control algorithm program is shown in Figure 4.

4 Application Effect

The external environment temperature dropped from 16℃ to -20℃. The application effect is shown in Figure 5. It took 510 s from the beginning to the basic stability (±1℃ difference from the set value). After the system stabilized, the fluctuation range was within ±0.8℃. The convergence speed and system stability are related to the quantization factor and the proportional factor. The quantization factor and the proportional factor are reasonably selected to achieve a balance between the convergence speed and stability.

5 Conclusion

This design adopts a control strategy based on fuzzy control theory to achieve reliable measurement and control of temperature and humidity in the environmental laboratory. It has the advantages of high precision, good stability, and fast convergence speed. Compared with the traditional switch control system, it has the advantages of precision, speed, and stability; compared with the prediction-based fuzzy control method, double fuzzy control strategy, and parameter self-learning fuzzy control strategy, it reduces the computational complexity. For environments with obvious coupling effects between temperature and humidity, the temperature and humidity can be decoupled and then controlled separately.